A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment
INFORMS Journal on Computing
New heuristics for no-wait flowshops to minimize makespan
Computers and Operations Research
Computers and Operations Research
The SR-GCWS hybrid algorithm for solving the capacitated vehicle routing problem
Applied Soft Computing
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
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A simulation-based algorithm for the Permutation Flowshop Sequencing Problem (PFSP) is presented. The algorithm uses Monte Carlo Simulation and a discrete version of the triangular distribution to incorporate a randomness criterion in the classical Nawaz, Enscore, and Ham (NEH) heuristic and starts an iterative process in order to obtain a set of alternative solutions to the PFSP. Thus, a random but biased local search of the space of solutions is performed, and a list of "good alternative solutions" is obtained. We can then consider several properties per solution other than the makespan, such as balanced idle times among machines, number of completed jobs at a given target time, etc. This allows the decision-maker to consider multiple solution characteristics apart from those defined by the aprioristic objective function. Therefore, our methodology provides flexibility during the sequence selection process, which may help to improve the scheduling process. Several tests have been performed to discuss the effectiveness of this approach. The results obtained so far are promising enough to encourage further developments and improvements on the algorithm and its applications in real-life scenarios. In particular, Multi-Agent Simulation is proposed as a promising technique to be explored in future works.